Medical device for detecting a ventricular arrhythmia event

ABSTRACT

A medical device and method for detecting a ventricular arrhythmia event is disclosed. The medical device includes input circuitry configured to receive an electrocardiogram (ECG) signal, processing circuitry coupled to the input circuitry and configured to identify at least one fiducial point of a first heartbeat signature and at least fiducial point of a second heartbeat signature of the ECG signal, and feature extraction circuitry coupled to the processing circuitry. The feature extraction circuitry is configured to determine at least one difference between the at least one fiducial point of the first heartbeat signal and the at least one fiducial point of the second heartbeat signal. Machine learning circuitry is coupled to the feature extraction circuitry and is configured to select a ventricular arrhythmia class based on the at least one difference.

RELATED APPLICATIONS

This application claims the benefit of U.S. provisional patentapplications No. 62/069,975, filed Oct. 29, 2014, and No. 62/074,409,filed Nov. 3, 2014, the disclosures of which are incorporated herein byreference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates to biomedical devices and methods todetect arrhythmias.

BACKGROUND

Sudden cardiac death (SCD) accounts for approximately 300,000 deaths inthe United States per year and in most cases is the final result ofventricular arrhythmias that include ventricular tachycardia (VT) orventricular fibrillation (VF). Ventricular arrhythmia is a severelyabnormal heart rhythm (arrhythmia) that, unless treated immediately, isresponsible for 75% to 85% of sudden deaths in persons with heartproblems. Most ventricular arrhythmias are caused by coronary heartdisease, hypertension, or cardiomyopathy, events that result inimmediate death if not accurately diagnosed or treated. VT is a fastrhythm of more than three consecutive beats originating from theventricles at rate of more than 100 beats per minute. VF is a rhythmcharacterized by chaotic activity of ventricles and causes immediatecessation of blood circulation and degenerates further into a pulselessor flat electrocardiogram record indicating no cardiac electricalactivity.

An implantable cardioverter-defibrillator (ICD) has been considered thebest protection against sudden death from ventricular arrhythmias inhigh risk individuals. However, most sudden deaths occur in individualswho do not have recognized high risk profiles. For long-term monitoring,electrocardiography is the criterion standard for the diagnosis ofventricular arrhythmia. If the clinical situation permits, a twelve leadelectrocardiogram (ECG) is obtained and analyzed before conversion ofthe rhythm to detect any changes in the characteristics of the ECGsignal. By extracting information about intervals, amplitude, andwaveform morphologies of the different P-QRS-T waves, the onset of theventricular arrhythmia can be detected. A wide range of algorithms anddetection systems based on morphological, spectral, or mathematicalparameters extracted from the ECG signal have been developed. Particularmethods have shown that a combination of ECG parameters extracted fromdifferent algorithms may enhance the performance of the detection.Although these methods have exhibited advantages in the detection ofventricular arrhythmia, there are disadvantages as well. Some methodshave proven quite difficult to implement or compute, while othersdemonstrate low specificity and low discrimination between normal andabnormal conditions. Moreover, most current methods involve a relativelylate detection interval, which delays the initiation of life savingmeasures.

Machine learning techniques such as neural networks and support vectormachines (SVM) have been suggested as useful tools to improve thedetection efficiency. However, this strategy increases the overallrequirements of the detection system if not utilized or employedproperly. For example, selected ECG parameters should be relevant andshow significant potential in the detection of ventricular arrhythmia.Otherwise, the efficiency of a machine learning task would decrease anddegrade overall performance. Thus, what is needed are a high performanceyet efficient medical device and method to enable early detection of theonset of ventricular arrhythmia.

SUMMARY

The present disclosure provides a high performance yet efficient medicaldevice and method for early detection of a ventricular arrhythmia event.The medical device includes input circuitry configured to receive anelectrocardiogram (ECG) signal, processing circuitry coupled to theinput circuitry and configured to identify at least one fiducial pointof a first heartbeat signature and at least one fiducial point of asecond heartbeat signature of the ECG signal, and feature extractioncircuitry coupled to the processing circuitry. The feature extractioncircuitry is configured to determine at least one difference between theat least one fiducial point of the first heartbeat signal and the atleast one fiducial point of the second heartbeat signal. Machinelearning circuitry is coupled to the feature extraction circuitry and isconfigured to select a ventricular arrhythmia class based on the atleast one difference. In at least one exemplary embodiment, the machinelearning circuitry includes a decision block that decides whether tooutput an alarm based upon the ventricular arrhythmia class selected bya classifier block.

Those skilled in the art will appreciate the scope of the disclosure andrealize additional aspects thereof after reading the following detaileddescription in association with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings incorporated in and forming a part of thisspecification illustrate several aspects of the disclosure, and togetherwith the description serve to explain the principles of the disclosure.

FIG. 1 is a schematic diagram depicting a medical device for detecting aventricular arrhythmia event of the present disclosure.

FIG. 2 is an ECG strip that depicts results of a formulation of T waveand P wave search windows with respect to a previously calculated RRinterval.

FIG. 3 is an ECG strip chart that depicts results of computations of Twave and P wave thresholds based on previously detected T peak, P peak,and R peak values.

FIG. 4 is an ECG strip chart that shows six ECG parameters that areusable with techniques of the present disclosure.

FIG. 5 is an ECG strip chart diagram of a related art ECG processingtechnique that uses processing windows that each contain only oneheartbeat signature.

FIG. 6 is an ECG strip chart diagram of an ECG processing techniqueusing processing windows that each contain two heartbeat signatures inaccordance with the present disclosure.

FIG. 7A is a box and whisker diagram depicting RT interval variabilitybetween GROUP A normal ECG samples and GROUP B abnormal ECG samples.

FIG. 7B is a box and whisker diagram depicting TR interval variabilitybetween GROUP A normal ECG samples and GROUP B abnormal ECG samples.

FIG. 7C is a box and whisker diagram depicting PQ interval variabilitybetween GROUP A normal ECG samples and GROUP B abnormal ECG samples.

FIG. 7D is a box and whisker diagram depicting QP interval variabilitybetween GROUP A normal ECG samples and GROUP B abnormal ECG samples.

FIG. 7E is a box and whisker diagram depicting PS interval variabilitybetween GROUP A normal ECG samples and GROUP B abnormal ECG samples.

FIG. 7F is a box and whisker diagram depicting SP interval variabilitybetween GROUP A normal ECG samples and GROUP B abnormal ECG samples.

FIG. 8 is an ECG strip chart illustrating QRS complex detection and Twave and P Wave delineation.

FIG. 9 is a graph depicting receiver operating characteristics (ROC)curves calculated for ventricular arrhythmia versus non-ventriculararrhythmia conditions.

FIG. 10A is a scatter plot of the SP parameter versus the RT parameter.

FIG. 10B is a scatter plot of the PQ parameter versus the RT parameter.

FIG. 10C is a scatter plot of the PS parameter versus the TR parameter.

FIG. 10D is a scatter plot of the SP parameter versus the TR parameter.

FIG. 10E is a scatter plot of the SP parameter versus the QP parameter.

FIG. 10F is a scatter plot of the PS parameter versus the RT parameter.

FIG. 10G is a scatter plot of the TR parameter versus the RT parameter.

FIG. 10H is a scatter plot of the QP parameter versus the TR parameter.

FIG. 10I is a scatter plot of the PS parameter versus the PQ parameter.

FIG. 10J is a scatter plot of the PS parameter versus the QP parameter.

FIG. 10K is a scatter plot of the QP parameter versus the RT parameter.

FIG. 10L is a scatter plot of the SP parameter versus the TR parameter.

FIG. 10M is a scatter plot of the PQ parameter versus the TR parameter.

FIG. 10N is a scatter plot of the QP parameter versus the PQ parameter.

FIG. 10O is a scatter plot of the SP parameter versus the PS parameter.

DETAILED DESCRIPTION

The embodiments set forth below represent the necessary information toenable those skilled in the art to practice the disclosure andillustrate the best mode of practicing the disclosure. Upon reading thefollowing description in light of the accompanying drawings, thoseskilled in the art will understand the concepts of the disclosure andwill recognize applications of these concepts not particularly addressedherein. It should be understood that these concepts and applicationsfall within the scope of the disclosure and the accompanying claims.

It will be understood that when an element such as a layer, region, orsubstrate is referred to as being “over,” “on,” “in,” or extending“onto” another element, it can be directly over, directly on, directlyin, or extend directly onto the other element or intervening elementsmay also be present. In contrast, when an element is referred to asbeing “directly over,” “directly on,” “directly in,” or extending“directly onto” another element, there are no intervening elementspresent. It will also be understood that when an element is referred toas being “connected” or “coupled” to another element, it can be directlyconnected or coupled to the other element or intervening elements may bepresent. In contrast, when an element is referred to as being “directlyconnected” or “directly coupled” to another element, there are nointervening elements present.

Relative terms such as “below” or “above” or “upper” or “lower” or“horizontal” or “vertical” may be used herein to describe a relationshipof one element, layer, or region to another element, layer, or region asillustrated in the Figures. It will be understood that these terms andthose discussed above are intended to encompass different orientationsof the device in addition to the orientation depicted in the Figures.

SECTION 1. INTRODUCTION

The present disclosure provides a high-performance yet efficient methodfor early detection of the onset of ventricular arrhythmia by combiningsix electrocardiogram (ECG) parameters. The six ECG parameters includePQ interval variability, QP interval variability, RT intervalvariability, TR interval variability, PS interval variability, and SPinterval variability. No combination of these parameters has previouslybeen used for detecting ventricular arrhythmia. However, the presentdisclosure demonstrates that the above six parameters are the mostsignificant set of parameters for the detection of ventriculartachycardia and ventricular fibrillation (VT/VF) events.

FIG. 1 is a schematic diagram depicting a medical device 10 of thepresent disclosure for detecting a ventricular arrhythmia event. Inparticular, the medical device 10 is a fully integrated ECG signalprocessing system suitable for real-time and efficient applicationsrequiring detection of a ventricular arrhythmia event. Medical device 10comprises input circuitry 12 that is configured to receive an ECGsignal. Processing circuitry 14 is coupled to the input circuitry 12 andis configured to identify at least one fiducial point of a firstheartbeat signature and the at least one fiducial point of a secondheartbeat signature, wherein each of the at least one fiducial point isassociated with at least one of the six ECG parameters. However, it isto be understood that the each of the at least one fiducial point is notlimited to just the six ECG parameters listed above. Other ECGparameters such as upper and lower envelope variations are also usable.

Feature extraction circuitry 16 is coupled to the processing circuitry14 and is configured to determine at least one difference between the atleast one first fiducial point of the first heartbeat signal and the atleast one first fiducial point of the second heartbeat signal. Machinelearning circuitry 18 is coupled to the feature extraction circuitry 16and is configured to select a ventricular arrhythmia class based on theat least one difference.

In more detail, the input circuitry 12 includes a low-pass filter 20 anda high-pass filter 22 that are configured to remove unwanted noisesignals coupled within the ECG signal. Once filtered, the ECG signal isreceived by the processing circuitry 14, which includes adifferentiation block 24 that takes a derivative of the filtered ECGsignal. A squaring block 26 is configured to square the derivative ofthe filtered ECG signal before a moving window integral block 28integrates data samples within the ECG signal that contains at least twoQRS complexes, two P waves and two T waves from at least two heartbeatsignatures. A QRS complex demarcation block 30 is configured to locatethe two or more QRS complexes. An R peak detection block 32 isconfigured to locate the R peaks within the QRS complexes once the QRScomplex demarcation block 30 provides demarcation of the QRS complexes.A Q onset and S offset detection block 34 is configured to search anddetect Q onsets and S offsets for each of the QRS complexes demarcated.

An RR demarcation block 36 is configured to determine the intervalbetween two R peaks detected by the R peak detection block 32.Typically, the two R peaks are automatically selected from twoconsecutive heartbeat signatures. A search window boundaries calculatorblock 38 is configured to perform calculations to determine searchwindow boundaries that will contain T wave and P wave fiducial points.The calculations performed take into consideration the samplingfrequency of the ECG signal. For instance, the search window boundariesmay select more sample points for a higher frequency ECG sampling. WhileFIG. 1 depicts the ECG sampling frequency as being 250 Hz, othersampling frequencies such as 360 Hz are usable with the search windowboundaries calculator block 38.

A T and P wave thresholds calculator block 40 is configured to calculateamplitude thresholds for the T waves and the P waves within the windowboundaries calculated by the search window boundaries calculator block38. A T wave delineation block 42 is configured to determine a preciselocation for each of the T waves using T wave amplitude thresholdsreceived from the T and P wave thresholds calculator block 40.Similarly, a P wave delineation block 44 is configured to determine aprecise location for each of the P waves using P wave amplitudethresholds received from the T and P wave thresholds calculator block40.

A fiducial point extraction block 46 is configured to find fiducialpoints within the calculated search window boundaries. The fiducialpoints extracted can be but are not limited to P peak, P offset, Qonset, R peak, S offset, T peak and T offset. Medical device 10 alongwith the following disclosed techniques take into account different ECGwaveform morphologies and utilize adaptive search windows along withthresholds to accurately detect the fiducial points of each heartbeat.

In an exemplary embodiment, the feature extraction circuitry 16 isconfigured to extract six parameters from search windows placed withinthe ECG signal. In this exemplary embodiment, the search window size isaround five seconds of an ECG signal. Once features are extracted,various other unique combinations of the parameters are constructed andused as input for the machine learning circuitry 18, which includes aclassification block 48 that is configured to classify the extractedfeatures and a decision block 50 that is configured to determine if aventricular arrhythmia event is occurring based upon the classificationof the extracted features.

In this regard, linear discriminant analysis (LDA) has been employed bythe machine learning circuitry 18 to distinguish healthy individualsfrom individuals susceptible to ventricular arrhythmia. The use of LDAby the machine learning circuitry introduces a strong potential fordetection of ventricular arrhythmia with a P value less than 0.001 whenusing ECG parameters. Secondly, a strong biasing effect of theclassification block 48 is avoided when using ECG parameters combinedwith the LDA. Thirdly, LDA is the simplest classification algorithm thatcan be employed using ECG parameters.

Five combinations of the six ECG parameters were evaluated by differentK-fold cross validations, which includes fivefold, sevenfold and tenfoldcross validations. The five combinations were constructed based upon anoutput rank of information gained feature selection technique. A bestperformance was found to be a combination that included all theextracted parameters using tenfold cross validation. Yet, theperformance of the other combinations also revealed good results.

Remaining portions of this disclosure are organized as follows. Insection II, ECG detection and delineation techniques are highlighted.Section III represents a feature construction stage along with analysisof building different combinations of the six ECG parameters. Aclassification algorithm implemented by classification block 48 isdescribed in section IV. Performance and results as well as a comparisonwith other detection methods are reported in section V.

SECTION 2. ECG SIGNAL PROCESSING

In order to detect the QRS complex, the Pan and Thompkins (PAT)algorithm is used. PAT is a commonly used algorithm based upon anamplitude threshold detection technique that exploits the fact that Rpeaks have higher amplitudes compared to other ECG wave peaks. Withproper pre-filtering of an ECG signal, the PAT algorithm is highlyefficient at detecting the R peaks in every heartbeat signature using anupper threshold level and lower threshold level.

A novel implementation of a delineation algorithm for the T and P wavesis provided in this disclosure. The delineation algorithm is based onadaptive search windows along with adaptive threshold levels toaccurately distinguish T and P peaks from noise peaks. In eachheartbeat, the QRS complex is used as a reference for the detection of Tand P waves in which two regions are demarcated with respect to aninterval between QRS complexes and is commonly referred to as the RRinterval. These regions are then used to form forward and backwardsearch windows of the T and P waves respectively, as shown in FIG. 2. Aforward search window is assumed to contain the T wave and theboundaries are extended from the QRS offset to two thirds of the RRinterval.

Positions of T and P peaks are registered by finding either a localmaximum or and a local minimum in each of the search windows and thencomparing them to the associated thresholds. A threshold for a T wave isgiven in equation 1, while a threshold for a P wave is given in equation2.

$\begin{matrix}{T_{{wave}_{th}} = {\frac{T_{peak}}{R_{peak}}t_{{thresh}_{in}}}} & (1) \\{P_{{wave}_{th}} = {\frac{P_{peak}}{R_{peak}}p_{{thresh}_{in}}}} & (2)\end{matrix}$

Each threshold given in equation 1 and equation 2 is modified in eachheartbeat signature based on the most recent detected values during apredetermined time period, such as five seconds. Scaling factorst_(thresh) _(in) and p_(thresh) _(in) are each set within a range of 0.1to 0.2 based on the most recent detected values in the last processingwindow. A technique for computing the thresholds is shown in FIG. 3. Bycomparing the local maximum and/or the local minimum points with thethresholds, the waveform morphology of each wave is identified. Forexample, the waveform morphology can be, but is not limited to, positivemonophasic, negative monophasic, or biphasic morphologies.

The delineation algorithm traces onset and offset values of the P-QRS-Twaves by finding a sample corresponding to a zero slope of a sampled ECGsignal. A sample point that has a zero slope and is located before thepeak is identified as the onset point. Similarly, the offset point isdetermined at the later side of the peak. At times, however, aderivative sign change occurs, which causes a false indicator. To solvethis problem, the delineation algorithm adds additional criteria for acorrect delineation of the wave boundaries based upon fiducial pointsthat tend to merge smoothly with an isoelectric line. The isoelectricline is approximated as the average value of the heartbeat signatureafter removing the QRS complex. The fact that the fiducial points tendto merge smoothly with the isoelectric line is used in combination withlocation of the zero slope point to accurately and reliably delineatethe fiducial points.

SECTION 3. FEATURE CONSTRUCTION

Feature construction begins when the machine learning circuitry 18compiles data from the ECG raw data signals. A selection of ECGparameters for a machine learning algorithm as implemented by themachine learning circuitry 18 of the present disclosure is an importantconsideration as selection of ECG parameters determines cost, runningtime, and overall performance of the medical device 10 that executes themachine learning algorithm by way of the machine learning circuitry 18.Once the machine learning circuitry 18 compiles data from the ECG rawdata signals, advanced ECG parameter extraction from the ECG raw datasignals can begin. In an exemplary embodiment the ECG data is analyzedand processed in a time window of five seconds to extract a set of sixparameters representing two consecutive cardiac states in every window.Moreover, the extracted parameters are normalized to the average maximalQRS deviation over an entire ECG recording and corrected with respect tothe RR interval to provide an accurate analysis regardless of the genderor age of the patient whose ECG is recorded. In an exemplary embodiment,the extracted parameters are mathematically independent of each other.

In at least one embodiment, at least 50 parameters have been extractedfrom an ECG signal based upon morphological, spectral, and mathematicalanalysis of the ECG signal. Exemplary ones of the 50 parameters arelisted in Table I below.

TABLE I EGC Parameters Used With The Exemplary EmibodimentsMorphological Spectral Mathematical Parameter Parameter ParameterIntervals Discrete Cosine Transform Hankel Transform Segments DiscreteFourier Transform Abel Transform Amplitudes Laplace Transform Areas AreaAsymmetry Interval Asymmetry Upper Envelope Variation Lower EnvelopeVariationSome of the ECG parameters listed in table 1 have been previouslydefined in other works and others are new in the detection field. Tochoose ECG parameters having a maximum discrimination characteristic fordetecting a ventricular arrhythmia event, statistical analysis of meanerror and standard deviation two-sided unpaired t-test and featureselection by filtering have been performed individually. In particular,the statistical analysis was used to assess separation between normaland abnormal ECG records. In the two-sided unpaired t-test, a P valueless than 0.001 in the 95% confidence interval (CI) has been consideredas statistically significant. Similarly, an area under a receiveroperating characteristics (ROC) curve, (AUC) is selected to be greaterthan 95% for further analysis. From this further analysis, an ECGparameter in the feature selection analysis has been selected to providethe highest arrhythmia detection accuracy generated from a single ECGparameter. This ECG parameter is combinable with other individual ECGparameters of high relevance to provide preferred combinations of thesix ECG parameters for even greater arrhythmia detection accuracy.

Section 3.1. ECG Databases

A study conducted in verification of the embodiments of the presentdisclosure included two groups, GROUP A and GROUP B. GROUP A includedECG records for persons having normal ECGs, while GROUP B includedpersons susceptible to ventricular arrhythmia. GROUP A included a set of18 single-lead normal ECG records obtained from the MassachusettsInstitute of Technology-Beth Israel Hospital (MIT/BIH) normal sinusrhythm database (NSRDB). The GROUP A ECG records were sampled at 250 Hzand had no significant arrhythmias. In contrast, GROUP B included 20single-lead abnormal ECG records with significant ventriculararrhythmias. The GROUP B abnormal ECG records were obtained fromdifferent sources including American Heart Association (AHA) Databaserecords sampled at 250 Hz, MIT-BIH ECG records sampled at 360 Hz, andCreighton University Database (CUDA) records sampled at 250 Hz. Table IIbelow provides additional details that match a particular database withparticular cardiac anomalies.

TABLE II EGC Records from Different Databases Database Quantity LengthArrhythmia Categories NSRDB 18 Records 24-Hours Not Applicable AHA 10Records  3-Hours Ventricular Tachycardia, Ventricular FlutterVentricular Fibrillation MIT-BIH  5 Records 30-Minutes VentricularTachycardia, Ventricular Flutter CUDA  5 Records  8-Minutes VentricularTachycardia, Ventricular Flutter Ventricular Fibrillation

Section 3.2 Short-Term ECG Parameters

A learning algorithm is strongly affected by the number and relevance ofinput variables. As such, analyses performed for this disclosure studiedthe ECG parameters listed in Table I. Each ECG parameter was examinedindependently with various discrimination techniques to determine themost analytically useful parameters. A unique set of six morphologicalECG parameters were found to be the most indicative characteristics ofventricular arrhythmia episodes. The set of six morphological ECGparameters includes PQ interval variability, QP interval variability, RTinterval variability, TR interval variability, PS interval variability,and SP interval variability.

FIG. 4 is an exemplary ECG graphic depicting the set of sixmorphological ECG parameters. The PQ interval represents the intervalfrom the atrial depolarization to the ventricular depolarization and ismeasured from the beginning of the P wave to the onset of the QRScomplex, while the QP interval is measured from the onset of the QRScomplex to the beginning of the P wave of the next cardiac cycle. The RTinterval is the duration of the ventricular systole in which theventricles remain in a depolarized state. The RT interval is measuredfrom the peak of the R wave to the start of the T wave. In contrast, theTR interval defines the ventricular diastole interval, which provides adetermination of how long the ventricles refill with blood followingcontraction.

The TR interval is measured from the start of the T wave of one cardiaccycle to the peak of the R wave of the next cardiac cycle. The timeinterval between the start of the P wave and the end of the S wave andbetween the end of the S wave of one cycle and the beginning of the Pwave of the next cycle define PS interval and SP interval, respectively.

FIG. 5 is an ECG strip chart diagram of a related art ECG processingtechnique that uses processing windows that each contain only oneheartbeat signature. Unlike other detection algorithms, which depend onthe common ECG parameters extracted from a single cardiac cycle as shownin FIG. 5, embodiments of the present disclosure process every twoconsecutive cycles together and relate pattern changes in the extractedECG parameters to ventricular arrhythmia as depicted in FIG. 6.

Section 3.3. Statistical Analysis

FIGS. 7A through 7F are box and whisker plots of the set of six ECGparameters comparing GROUP A with GROUP B. Table III below providestabulated data for the box and whisker plots depicted in FIGS. 7Athrough 7F.

TABLE III Statistical Analysis of the Set of Six EGC Parameters μ ± α μ± α Parameter GROUP A GROUP B p-value PQ Interval Variability 0.073 ±0.0538  0.0222 ± 0.0395 <0.001 QP Interval Variability 0.5311 ± 0.0532   1.18 ± 0.003 <0.001 RT Interval Variability 0.322 ± 0.0067 0.607 ±1723 <0.001 TR Interval Variability 0.283 ± 0.095  0.596 ± 1742 <0.001PS Interval Variability 0.21 ± 0.057  0.144 ± 0.071 <0.001 SP IntervalVariability 0.396 ± 0.062    1.065 ± 0.0598 <0.001

The statistics listed in Table III illustrate discernable delineationsbetween the set of six ECG parameters for each of GROUP A and GROUP Bfor a p<0.001. For example, the mean value of the PQ intervalvariability is slightly greater for GROUP A and GROUP B, and a similarobservation is made for the PS interval variability. However, the QPinterval variability, the RT interval variability, the TR intervalvariability, and the SP interval variability have significantly higherdelineations between the set of six ECG parameters for GROUP A and GROUPB for a p<0.001. In particular, the mean error in GROUP B is at leasttwice the mean error of GROUP A.

Section 3.4. Information Gain Attribute Evaluation

Filter-based feature selection (FS) was used to prioritize delineationefficiency of the six ECG parameters. Filter-based FS is independent ofthe machine learning classifier of the machine learning circuitry 18(FIG. 1) and uses an attribute evaluator and a ranker to rank all theparameters in the original data set. In this disclosure, an informationgain (IG) attribute evaluator was applied.

Entropy, which measures a system's unpredictability, is used as thefoundation for the IG attribute evaluator. The entropy of Y, H(Y), isgiven in equation 3.

$\begin{matrix}{{H(Y)} = {- {\sum\limits_{y \in Y}\; {{p(y)}\mspace{14mu} {\log_{2}( {p(y)} )}}}}} & (3)\end{matrix}$

where p(y) is the marginal probability density function for the randomvariable Y. In some cases the observed values of Y in the training dataset are partitioned according to the values of the second feature X Inthis case, the entropy of Y after observing X is given in equation 4.

$\begin{matrix}{{H( Y \middle| X )} = {- {\sum\limits_{x \in X}\; {{p(x)}{\sum\limits_{y \in Y}\; {{p( y \middle| x )}\mspace{14mu} {\log_{2}( {p( y \middle| x )} )}}}}}}} & (4)\end{matrix}$

where p(y|x) is the conditional probability of y given x. The IGmeasurement reflects information about Y provided by X and is given byequation 5.

IG=H(Y)−H(Y|X)  (5)

In this disclosure, Y is the class (GROUP A and GROUP B) and X is thevector containing the six ECG parameters.

SECTION 4. CLASSIFICATION MODEL

Embodiments of this disclosure use linear discriminant analysis (LDA), atechnique developed by R. A. Fisher in 1936 to discriminate ventriculararrhythmia versus non-ventricular arrhythmia. In particular, theparameters PQ interval variability, QP interval variability, PS intervalvariability, SP interval variability, RT interval variability, and TRinterval variability are extracted from ECG signals to produce a newdata set that is processed using LDA to discriminate ventriculararrhythmia versus non-ventricular arrhythmia. LDA is executed in theclassification block 48 (FIG. 1) of the machine learning circuitry 18.

LDA is mathematically robust and produces models with accuracyequivalent to more complex delineation methods when input variables havea strong correlation with the monitored ECG signal. As such, embodimentsof the present disclosure use LDA to perform classification.

In an exemplary embodiment, a projection of samples x onto a line y isgiven by equation 6.

y=w ^(T) x  (6)

The goal of implementing LDA is to provide a relatively large separationbetween the class means, while also keeping the in-class variancerelatively small. A mathematical formulation of this goal is realized bymaximizing the Fisher criterion J(w), which is given in equation 7.

$\begin{matrix}{{J(w)} = \frac{{\overset{\sim}{\mu_{1}} - \overset{\sim}{\mu_{2}}}}{\overset{\sim}{s_{1}^{2}} + \overset{\sim}{s_{2}^{2}}}} & (7)\end{matrix}$

{tilde over (μ)} is the main vector of each class in the y featurespace, given in equation 8.

$\begin{matrix}{\overset{\sim}{\mu} = {{\frac{1}{N_{i}}{\sum\limits_{y \in w_{i}}\; y}} = {\frac{1}{N_{i}}{\sum\limits_{x \in w_{i}}\; {w^{T}x}}}}} & (8)\end{matrix}$

{tilde over (s)}² is the variance, given in equation 9.

$\begin{matrix}{{\overset{\sim}{s}}^{2} = {\sum\limits_{y \in w_{i}}\; ( {y - \overset{\sim}{\mu_{i}^{2}}} )}} & (9)\end{matrix}$

The final Fisher criterion J(w), can be rewritten by defining thebetween-class variable (S_(B)) and the within-class variable (S_(W))given in equations 10 and 11, respectively.

S _(B)=(μ₁−μ₂)(μ₁−μ₂)^(T)  (10)

$\begin{matrix}{S_{W} = {\sum\limits_{{i = 1},2}\; {\sum\limits_{n = 1}^{N_{i}}\; {( {x_{n}^{i} - \mu_{i}} )( {x_{n}^{i} - \mu_{i}} )^{T}}}}} & (11)\end{matrix}$

Thus, the final Fisher Criterion J(w), can be re-written as given inequation 12.

$\begin{matrix}{{J(w)} = \frac{w^{T}S_{B}w}{w^{T}S_{W}w}} & (12)\end{matrix}$

By differentiating J(w), with respect to w, and setting the result tozero, a generalized eigenvalue problem yields equation 13, whichspecifies a choice of direction for a projection of data down to 1-d.

w=S _(W) ⁻¹(μ₁−μ₂)  (13)

An analysis of the classification procedure randomly divided eachparameter data set into different training, testing, and validation datasets to determine maximum classification performance. During trainingand testing, 64% of the parameter data was used for training theclassifier, whereas the remaining 36% was split equally into a testingdata set and a validation data set. A training and testing procedure wasthen repeated several times to ensure that results were independent ofintroduced randomization.

Various combinations of the selected training parameters were fed intothe LDA model as input and then the models were evaluated on thecorresponding combination test data. Each combination was validatedusing ten K-fold cross validations on the parameter data set. An averageof the K fold cross validations was ultimately used for evaluation.

SECTION 5. PERFORMANCE AND RESULTS Section 5.1. ECG Signal Detection andDelineation

The performance of the implemented QRS complex demarcation block 30(FIG. 1) was assessed by evaluating the sensitivity (SE) and precision(P) as shown in equations 14 and 15.

$\begin{matrix}{{SE} = \frac{TP}{{TP} + {FN}}} & (14) \\{P = \frac{TP}{{TP} + {FP}}} & (15)\end{matrix}$

where TP is a variable representing true detections, FN is a variablerepresenting false negative detections, and FP is a variablerepresenting false positive detections. During testing, the QRS complexdemarcation block 30 achieved a sensitivity SE=99.8% and a precision ofP=98.6%.

Moreover, the mean error (μ) and the standard deviation (σ) of thefiducial points including the P peak, the P offset, the Q onset, the Rpeak, the S offset, the T peak, and T offset were calculated between theannotated and automated results, which are listed table IV below.

TABLE IV PERFORMANCE EVALUATION OF THE ECG SIGNAL PROCESSING ALGORITHMParameter P_(peak) P_(off) Q_(on) R_(peak) S_(off) T_(peak) T_(off) μ(ms) 5.5050 −2.5962 −4.9719 −1.1025 −4.9719 −1.3671 6.3682 σ (ms) 8.64677.9140 6.7037 4.5076 6.7037 12.0788 14.6465

FIG. 8 is an annotated ECG chart that is representative of the resultsof QRS complex detection along with T wave and P wave delineation usingthe embodiments of the present disclosure. FIG. 8 illustrates that thedetection and delinition algorithm has accurately identified all of thefiducial points within a preselected search window that captures everyheartbeat signature.

Section 5.2. Performance of Individual Parameters

Table V shows the rank of the six EGC parameter sorted by the IG featureselection. The ranking was used to form the different combinations ofthe ECG parameters. The performances of the individual ECG parametersbased on the LDA, and using training and test data set with a fivesecond sampling window length, is presented in Table VI. Accuracy (ACC)is calculated using equation 16.

$\begin{matrix}{{ACC} = \frac{{TP} + {TN}}{{TP} + {TN} + {FP} + {FN}}} & (16)\end{matrix}$

The individual discrimination ability of each ECG parameter was studiedby analyzing ROC curves shown in FIG. 9. The performance of theparameters was assessed by determining the area under the ROC curves(AUC) as shown in FIG. 9. All of the parameters provided goodperformance having an AUC greater than 99%. Table V below lists theranking of each of the six ECG parameters. A ranking analysis of the ECGparameters was conducted using filter-based feature selection.

TABLE V RANKING ANALYSIS OF ECG PARAMETERS USING FS BY FILTER RankParameters 1 QP interval variability 2 SP interval variability 3 RTinterval variability 4 TR interval variability 5 PQ interval variability6 PS interval variability

Section 5.3. Performance of Parameter Combinations

Different unique combinations of the ECG parameters were tested to findout the set with the maximum accuracy. A first combination contained thetop two ranked parameters including the QP interval variability and theSP interval variability. Next, for each new combination, a new parameterwas added until a final combination included all six ECG parameters.Please see Table VI.

TABLE VI PERFORMANCE OF THE INDIVIDUAL PARAMETERS USED IN THIS WORK(WINDOW SIZE = 5 SEC) Training set (%) Testing set (%) Parameter ACC SEP AUC ACC SE P AUC QP int. var. 99.4 99.3 99.4 99.82 99.1 96 99.8 99.3SP int. var. 99.66 99.7 97.8 99.7 99.3 95.4 99.5 99.5 RT int. var. 98.8497.5 97.8 99.5 98.26 95.3 99.6 99.64 TR int. var. 97.16 97.2 97.7 99.897.1 93.1 98.9 99.06 PQ int. var. 96.78 96.8 96 99.09 96.9 93.9 98.899.01 PS int. var. 96.24 95.5 95.22 99.004 96.4 95.5 98.9 99.01 int. ≡interval, var. ≡ variability

Table VII lists the performance of the ECG parameter combinations usinga five second window length. Note that the maximum accuracy obtained bytraining and testing was the fifth combination. As such, combininginformation from all six ECG parameters provides the most robustdetection system for cardiac arrhythmia and/or other cardiac failuresignals.

TABLE VII PERFORMANCE OF THE PARAMETER COMBINATIONS USED IN THIS WORK(WINDOW SIZE = 5 SEC) Combination Training set (%) Testing set (%)number Combination parameters ACC SE P AUC ACC SE P AUC #1 QP, SPinterval variability 98.89 98.1 96.2 99.81 98.95 96.1 99.36 99.88 #2 QP,SP, RT interval variability 99.01 99.2 98.4 99.909 99.071 96.4 99.299.901 #3 QP, SP, RT, TR interval variability 97.21 98.3 95.2 99.7 97.195.2 98.3 99.67 #4 QP, SP, RT, TR, PQ interval variability 99.13 97.198.24 99.9 99.3 98.9 99.44 99.05 #5 QP, SP, RT, TR, PQ, PS intervalvariability 98.98 98.9 98.99 99.96 99.1 97.5 99.4 99.95

Section 5.4. Validation Results

The performance of the LDA classifier was analyzed using eachcombination independently. Different K-fold cross validations wereinvestigated using the study data set repeated 10 times for eachprocedure. A sample average performance of fivefold, sevenfold andtenfold cross validations are shown below in Tables VIII, IX, and Xrespectively.

TABLE VIII FIVEFOLD VALIDATION RESULTS OF THE PARAMETER COMBINATIONS(WINDOW SIZE = 5 SEC) Combination Combination Validation set (%) numberparameters ACC SE P AUC #1 QP, SP interval variability 98.3 98.51 97.4299.86 #2 QP, SP, RT 98.37 98.601 98.1 99.89 interval variability #3 QP,SP, RT, TR 98.16 98.5 98.84 99.84 interval variability #4 QP, SP, RT,TR, PQ 98.8 98.74 98.54 99.90 interval variability #5 QP, SP, RT, TR,PQ, 99.02 98.92 98.41 99.96 PS interval variability

TABLE IX SEVENFOLD VALIDATION RESULTS OF THE PARAMETER COMBINATIONS(WINDOW SIZE = 5 SEC) Combination Combination Validation numberparameters ACC SE P AUC #1 QP, SP interval variability 98.42 98.55 97.4299.86 #2 QP, SP, RT 98.45 98.65 98.13 99.89 interval variability #3 QP,SP, RT, TR 98.26 98.59 98.84 99.85 interval variability #4 QP, SP, RT,TR, PQ 98.83 98.80 98.60 99.91 interval variability #5 QP, SP, RT, TR,PQ, 99.08 98.94 98.44 99.96 PS interval variability

TABLE X SEVENFOLD VALIDATION RESULTS OF THE PARAMETER COMBINATIONS(WINDOW SIZE = 5 SEC) Combination Combination Validation numberparameters ACC SE P AUC #1 QP, SP interval variability 98.50 98.54 97.3899.87 #2 QP, SP, RT 98.47 98.61 98.18 99.89 interval variability #3 QP,SP, RT, TR 98.32 98.60 98.81 99.86 interval variability #4 QP, SP, RT,TR, PQ 98.88 98.81 98.63 99.91 interval variability #5 QP, SP, RT, TR,PQ, 99.1 98.95 98.39 99.97 PS interval variability

The most accurate sample performance is indicated by the fifthcombination with any K-fold cross validation values. An ACC of 99.02%,an SC of 98.92%, and a P of 98.41% were obtained by the fivefold crossvalidation. By increasing the number of folds to seven, the ACC, the SE,and the P were improved by 0.06%, 0.02%, and 0.03%, respectively. Thetenfold cross validation achieved the most accurate overall results withan ACC of 99.1%, an SE of 98.95%, and a P of 98.39%. The AUC values formost of the combinations with any K-fold cross validations weresubstantial as well.

FIGS. 10A through 10O are scatterplots for the ECG parameter pairs ofthe fifth combination using LDA for a tenfold cross validation. Notethat the independency between most parameter pairs is substantialbetween GROUP A and GROUP B. The relatively strong delineation providedby LDA is graphically illustrated in FIGS. 10A through 10O consideringthe substantial separation between the GROUP A and the GROUP B parameterpairs.

SECTION 6. CONCLUSION

This disclosure provides the medical device 10 (FIG. 1), which is a newapparatus and detection method for determining a ventricular arrhythmiaevent. The medical device 10 combines a unique set of ECG parameterswith the LDA algorithm. The six selected ECG parameters represent thestatus of two consecutive heartbeat signatures. To date, these six ECGparameters have not been used for ventricular arrhythmia detection.However, applicants of this disclosure discovered that each of these sixnew ECG parameters used by the medical device 10 provide unprecedentedaccuracy and efficiency for the detection of ventricular arrhythmia witha p-value that is less than 0.001.

While the QRS complex is detected using related art techniques, such asPAT, a new P and T delineation technique is used to accurately andindependently identify T waves and P waves. The new technique updates Pwave and T wave delineation with each heartbeat based upon previouslydetected P waves and T waves. This delineation is achieved in the timedomain without the need for spectral or transformation analysis of theECG signal, which reduces the overall complexity of the medical device10.

Moreover, the six ECG parameters are novel and include: PQ intervalvariability, QP interval variabilty, RT interval variability, TRinterval variability, PS interval variability, and SP intervalvariability. The six ECG parameters are morphological, which providesbenefits that include less processing time and fewer computationscompared to traditional methods used to monitor for ventriculararrhythmia. Based upon statistical ROC analysis, the six ECG parametersindividually and in combinations result in robust and accurateventricular arrhythmia detection.

A Fisher LDA classifier is used to separate ventricular arrhythmia andnon-ventricular arrhythmia. Despite the relative simplicity of theFisher LDA, the differentiation between ventricular arrhythmia andnon-ventricular arrhythmia is relatively high in comparison totraditional techniques used to differentiate between ventriculararrhythmia and non-ventricular arrhythmia. This relatively strongperformance is not attributable to the Fisher LDA, but is ratherattributable to the relevance between the six ECG parameters and theircorrelation to differences between ventricular arrhythmia andnon-ventricular arrhythmia.

Those skilled in the art will recognize improvements and modificationsto the embodiments of the present disclosure. All such improvements andmodifications are considered within the scope of the concepts disclosedherein and the claims that follow.

What is claimed is:
 1. A medical device for detecting a ventriculararrhythmia event comprising: input circuitry configured to receive anelectrocardiogram (ECG) signal; processing circuitry coupled to theinput circuitry and configured to identify at least one fiducial pointof a first heartbeat signature and the at least one fiducial point of asecond heartbeat signature of the ECG signal; feature extractioncircuitry coupled to the processing circuitry and configured todetermine at least one difference between the at least one fiducialpoint of the first heartbeat signal and the at least one fiducial pointof the second heartbeat signal; and machine learning circuitry coupledto the feature extraction circuitry and configured to select aventricular arrhythmia class based on the at least one difference. 2.The medical device of claim 1 wherein the first heartbeat signature andthe second heartbeat signature are generated from two consecutiveheartbeats.
 3. The medical device of claim 2 wherein the at least onedifference between the at least one fiducial point of the firstheartbeat signal and the at least one fiducial point of the secondheartbeat signal are selected from at least three parameters of thegroup consisting of QP interval variability, SP interval variability, RTinterval variability, TR interval variability, PQ interval variability,and PS interval variability.
 4. The medical device of claim 1 whereinthe at least one difference between the at least one fiducial point ofthe first heartbeat signal and the at least one fiducial point of thesecond heartbeat signal is QP interval variability.
 5. The medicaldevice of claim 1 wherein the at least one difference between the atleast one fiducial point of the first heartbeat signal and the at leastone fiducial point of the second heartbeat signal is SP intervalvariability.
 6. The medical device of claim 1 wherein the at least onedifference between the at least one fiducial point of the firstheartbeat signal and the at least one fiducial point of the secondheartbeat signal is RT interval variability.
 7. The medical device ofclaim 1 wherein the at least one difference between the at least onefiducial point of the first heartbeat signal and the at least onefiducial point of the second heartbeat signal is TR intervalvariability.
 8. The medical device of claim 1 wherein the at least onedifference between the at least one fiducial point of the firstheartbeat signal and the at least one fiducial point of the secondheartbeat signal is PQ interval variability.
 9. The medical device ofclaim 1 wherein the at least one difference between the at least onefiducial point of the first heartbeat signal and the at least onefiducial point of the second heartbeat signal is PS intervalvariability.
 10. The medical device of claim 1 wherein the at least onedifference between the at least one fiducial point of the firstheartbeat signal and the at least one fiducial point of the secondheartbeat signal is QP interval variability and SP interval variability.11. The medical device of claim 1 wherein the at least one differencebetween the at least one fiducial point of the first heartbeat signaland the at least one fiducial point of the second heartbeat signal is QPinterval variability, SP interval variability, and RT intervalvariability.
 12. The medical device of claim 1 wherein the at least onedifference between the at least one fiducial point of the firstheartbeat signal and the at least one fiducial point of the secondheartbeat signal is QP interval variability, SP interval variability, RTinterval variability, and TR interval variability.
 13. The medicaldevice of claim 1 wherein the at least one difference between the atleast one fiducial point of the first heartbeat signal and the at leastone fiducial point of the second heartbeat signal is QP intervalvariability, SP interval variability, RT interval variability, TRinterval variability, and PQ interval variability.
 14. The medicaldevice of claim 1 wherein the at least one difference between the atleast one fiducial point of the first heartbeat signal and the at leastone fiducial point of the second heartbeat signal is QP intervalvariability, SP interval variability, RT interval variability, TRinterval variability, PQ interval variability, and PS intervalvariability.
 15. A method for a medical device for detecting aventricular arrhythmia event comprising: receiving an electrocardiogram(ECG) signal by way of input circuitry; identifying at least onefiducial point of a first heartbeat signature and the at least onefiducial point of a second heartbeat signature of the ECG signal by wayof processing circuitry coupled to the input circuitry; determining atleast one difference between the at least one fiducial point of thefirst heartbeat signal and the at least one fiducial point of the secondheartbeat signal by way of feature extraction circuitry coupled to theprocessing circuitry; and selecting a ventricular arrhythmia class basedon the at least one difference by way of machine learning circuitrycoupled to the feature extraction circuitry.
 16. The method for themedical device of claim 15 wherein the first heartbeat signature and thesecond heartbeat signature are generated from two consecutiveheartbeats.
 17. The method for the medical device of claim 16 whereinthe at least one difference between the at least one fiducial point ofthe first heartbeat signal and the at least one fiducial point of thesecond heartbeat signal are selected from at least three parameters ofthe group consisting of QP interval variability, SP intervalvariability, RT interval variability, TR interval variability, PQinterval variability, and PS interval variability.
 18. The method forthe medical device of claim 15 wherein the at least one differencebetween the at least one fiducial point of the first heartbeat signaland the at least one fiducial point of the second heartbeat signal is QPinterval variability, SP interval variability, and RT intervalvariability.
 19. The method for the medical device of claim 15 whereinthe at least one difference between the at least one fiducial point ofthe first heartbeat signal and the at least one fiducial point of thesecond heartbeat signal is QP interval variability, SP intervalvariability, RT interval variability, and TR interval variability. 20.The method for the medical device of claim 15 wherein the at least onedifference between the at least one fiducial point of the firstheartbeat signal and the at least one fiducial point of the secondheartbeat signal is QP interval variability, SP interval variability, RTinterval variability, TR interval variability, and PQ intervalvariability.
 21. The method for the medical device of claim 15 whereinthe at least one difference between the at least one fiducial point ofthe first heartbeat signal and the at least one fiducial point of thesecond heartbeat signal is QP interval variability, SP intervalvariability, RT interval variability, TR interval variability, PQinterval variability, and PS interval variability.